University of Cambridge > > The Microsoft AI Residency > An Introduction to Simple Markov Models

An Introduction to Simple Markov Models

Add to your list(s) Download to your calendar using vCal

If you have a question about this talk, please contact Microsoft Research Cambridge Talks Admins.

 Please note, this event may be recorded. Microsoft will own the copyright of any recording and reserves the right to distribute it as required

Sequence data can be found everywhere: from base pairs in a DNA molecule to characters in handwritten text. We need models for sequences to predict or forecast future data, to remove noise, and to aid understanding (e.g. by identifying underlying latent variables). Markov models are the simplest such models. They can be used in their own right, or composed to form more complex models. In this tutorial I will give a very short introduction to Markov models for discrete valued data (N-gram models) and real-valued data (auto-regressive models). The goal is to give an intuitive feel for these models, rather than an exhaustive exposition. If time permits, I will show how they can be composed with more complex components, such as neural networks, to perform complex tasks.

This talk is part of the The Microsoft AI Residency series.

Tell a friend about this talk:

This talk is included in these lists:

Note that ex-directory lists are not shown.


© 2006-2024, University of Cambridge. Contact Us | Help and Documentation | Privacy and Publicity